NAWRUmodel {RGAP}

NAWRU model

Description

Creates a state space object object of class NAWRUmodel which can be fitted using fit.

Usage

NAWRUmodel(
  tsl,
  trend = "RW2",
  cycle = "AR2",
  type = "TKP",
  cycleLag = 0,
  pcErrorARMA = c(0, 0),
  exoType = NULL,
  start = NULL,
  end = NULL,
  anchor = NULL,
  anchor.h = NULL
)

Arguments

tsl

A list of time series objects, see details.

trend

A character string specifying the trend model. trend = "RW1" denotes a first order random walk, trend = "RW2" a second order random walk (local linear trend) and trend = "DT" a damped trend model. The default is trend = "RW2".

cycle

A character string specifying the cycle model. cycle = "AR1" denotes an AR(1) process, cycle = "AR2" an AR(2) process. The default is cycle = "AR2".

type

A character string specifying the type of the Phillip's curve. type = "TKP" denotes the traditional Keynesian Phillip's curve and type = "NKP" the New Keynesian Phillip's curve, see details. The default is type = "TKP".

cycleLag

A vector specifying the cycle lags that are included in the Phillip's curve. The default is cycleLag = 0, see details.

pcErrorARMA

A 2 x 1 vector with non-negative integers specifying the AR and MA degree of the error term in the Phillip's curve equation. The default is pcErrorARMA = c(0, 0), see details.

exoType

An optional n x m x 2 array specifying the possible difference and lag transformation for the variables. exoType can be initialized using the function inizializeExo. The column names give the variable names. exoType[, , 1] contains the difference transformations and exoType[, , 2] the subsequent lag transformations, see details.

start

(Optional) Start vector for the estimation, e.g. c(1980, 1).

end

(Optional) End vector for the estimation, e.g. c(2020, 1).

anchor

(Optional) Anchor value for the unemployment rate.

anchor.h

(Optional) Anchor horizon in the frequency of the given time series.

Details

The list of time series tsl needs to have the following components:

ur

Unemployment rate.

nulc

Nominal Unit labor costs, if type = "TKP".

rulc

Real unit labor costs, if type = "NKP".

and optionally other variables included in exoType.

A cycleLag equal to 0 implies that only the contemporaneous cycle is included in the Phillip's curve. A cycleLag equal to 0:1 implies that the contemporaneous as well as the lagged cycle are included.

A pcErrorARMA equal to c(0, 0) implies that the error term in the Phillip's curve is white noise. pcErrorARMA = c(1, 0) implies that the error is an AR(1) process and for pcErrorARMA = c(1, 2) the error follows an ARMA(1, 2) process.

For the New Keynesian Phillip's curve, the cycleLag cannot be chosen. cycleLag will be set to 0 if cycle = "AR1" and to 1 if cycle = "AR2". In the latter case, the forward solution of the Phillip's curve implies parameter restrictions for the lagged cycle on the Phillip's curve. Moreover, exogenous variables will be ignored in the case of the New Keynesian Phillip's curve.

The array exoType consists of non-negative integers or NAs. exoType[, , 1] = c(NA,1) and exoType[, , 2] = c(NA,2) implies that the first variable is not included in the Phillip's curve whereas the second lag of the first difference of the second variable is included.

Value

Object of class NAWRUmodel, which is a list with the following components:

tsl

A list of used time series.

SSModel

An object of class SSModel specifying the state-space model.

loc

A data frame containing information on each involved parameter, for instance its corresponding system matrix, variable names, and parameter restrictions.

call

Original call to the function.

In addition, the object contains the following attributes:

cycle

Cycle specification.

trend

Trend specification.

phillipsCurve

A list containing the components type, cycleLag, errorARMA, exoVariables.

anchor

A list containing the components value, horizon.

period

A list containing the components start, end, frequency.

Examples

# load data for France
data("gap")
tsList <- amecoData2input(gap$France, alpha = 0.65)
# Traditional phillips curve
model <- NAWRUmodel(tsl = tsList, trend = "RW2", cycle = "AR2", type = "TKP", cycleLag = 0)

# New-Keynesian Phillips curve
model <- NAWRUmodel(tsl = tsList, trend = "RW2", cycle = "AR2", type = "NKP", cycleLag = 0:1)

# Traditional Phillips curve with 6 exogenous variables
# specify exogenous variable transformations
D <- matrix(c(2, 2, 2, 1, 1, 1), 2, 3, byrow = TRUE)
L <- matrix(c(0, 0, 0, 1, 1, 1), 2, 3, byrow = TRUE)
exoType <- initializeExo(varNames = c("tot", "prod","ws"), D = D, L = L)
model <- NAWRUmodel(tsl = tsList, cycleLag = 0:1, exoType = exoType)